import torch

'自定义锚框'
ANCHORS_GROUP = {
    13: [[360, 360], [360, 180], [180, 360]],
    26: [[180, 180], [180, 90], [90, 180]],
    52: [[90, 90], [90, 45], [45, 90]]
}

'yolo5 coco数据集锚框'
ANCHORS_DIC = {
    13: [[116, 90], [156, 198], [373, 326]],
    26: [[30, 61], [62, 45], [59, 119]],
    52: [[10, 13], [16, 30], [33, 23]]
}

device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
CLASS_NUM = 4
IMG_ORI_SIZE = 416
BASE_IMG_PATH = r'E:\pythonProject\yolo3\data\VOC2007\YOLOv3_JPEGImages'
BASE_LABEL_PATH = r'E:\pythonProject\yolo3\data\VOC2007\yolo_annotation.txt'
WEIGHT_PATH = r'E:\pythonProject\yolo3\net\weights\best.pt'

SCALE_FACTOR_BIG = 0.9
SCALE_FACTOR_MID = 0.9
SCALE_FACTOR_SML = 0.9
'阈值'
THRESHOLD_BOX = 0.9
THRESHOLD_NMS = 0.1

'视频路径'
VIDEO_PATH = r'E:\pythonProject\yolo3\data\video\fish_video.mp4'
VIDEO2FRAME_PATH = r'E:\pythonProject\yolo3\data\VOC2007\JPEGImages'
'网络参数'
DARKNET35_PARAM_PATH = r'E:\pythonProject\yolo3\config\data.yaml'
'检测类别'
CLS_DIC = {
    0: 'big_fish',
    1: 'small_fish'
}
COLOR_DIC = {0: (0, 0, 255), 1: (100, 200, 255), 2: (255, 0, 0), 3: (0, 255, 0)}
